Invention Grant
- Patent Title: Block floating point computations using shared exponents
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Application No.: US15974643Application Date: 2018-05-08
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Publication No.: US10579334B2Publication Date: 2020-03-03
- Inventor: Daniel Lo , Eric Sen Chung
- Applicant: Microsoft Technology Licensing, LLC
- Applicant Address: US WA Redmond
- Assignee: Microsoft Technology Licensing, LLC
- Current Assignee: Microsoft Technology Licensing, LLC
- Current Assignee Address: US WA Redmond
- Main IPC: G06F7/483
- IPC: G06F7/483 ; G06F7/499 ; G06F7/523 ; G06F7/491

Abstract:
A system for block floating point computation in a neural network receives a plurality of floating point numbers. An exponent value for an exponent portion of each floating point number of the plurality of floating point numbers is identified and mantissa portions of the floating point numbers are grouped. A shared exponent value of the grouped mantissa portions is selected according to the identified exponent values and then removed from the grouped mantissa portions to define multi-tiered shared exponent block floating point numbers. One or more dot product operations are performed on the grouped mantissa portions of the multi-tiered shared exponent block floating point numbers to obtain individual results. The individual results are shifted to generate a final dot product value, which is used to implement the neural network. The shared exponent block floating point computations reduce processing time with less reduction in system accuracy.
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